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Moral dilemmas for moral machines

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Abstract

Autonomous systems are being developed and deployed in situations that may require some degree of ethical decision-making ability. As a result, research in machine ethics has proliferated in recent years. This work has included using moral dilemmas as validation mechanisms for implementing decision-making algorithms in ethically-loaded situations. Using trolley-style problems in the context of autonomous vehicles as a case study, I argue (1) that this is a misapplication of philosophical thought experiments because (2) it fails to appreciate the purpose of moral dilemmas, and (3) this has potentially catastrophic consequences; however, (4) there are uses of moral dilemmas in machine ethics that are appropriate and the novel situations that arise in a machine-learning context can shed some light on philosophical work in ethics.

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Notes

  1. So too have attempts to codify principles for ethical AI research, though largely to little effect. See Jobin et al. [58] for a recent survey; see also LaCroix and Mohseni [66] for a discussion of the efficacy of such proposals.

  2. For example, the goal of the Dartmouth Summer Research Project on Artificial Intelligence, held in 1956 and organised by John McCarthy, Marvin Minsky, Nathaniel Rochester, and Claude Shannon, was stated as follows:

    The study is to proceed on the basis of the conjecture that every aspect of learning or any other feature of intelligence can in principle be so precisely described that a machine can be made to simulate it. An attempt will be made to find how to make machines use language, form abstractions and concepts, solve kinds of problems now reserved for humans, and improve themselves. We think that a significant advance can be made in one or more of these problems if a carefully selected group of scientists work on it together for a summer.

    Of course, these are all still open problems today [86].

  3. The individual tasks—detection, recognition, localisation, prediction, action—can be accomplished using several different methods—including regression, pattern recognition, clustering, and decision matrices, often involving a plethora of state-of-the-art machine-learning techniques. For example, support vector machines and principal component analysis may be used for pattern recognition; K-means clustering may be used to identify data in low-resolution images; and gradient-boosting may be used for decision-making, depending on confidence levels for detection, classification, and prediction. Advances are continually being made in this field, with some focus on end-to-end learning; for example, Bojarski et al. [19], Yang et al. [106] employ convolutional neural networks to train their vehicles without requiring the highly complex suite of algorithms used in traditional methods. Kuefler et al. [64] use Generative Adversarial Networks to train their system by mimicking human behaviour.

  4. In fact, full automation is not necessary for the problems described to arise since human reaction time will not be useful in split-second moral decisions.

  5. In the last few years alone, there have been dozens of articles that refer to Philippa Foot’s 1967 paper in the context of autonomous vehicles; see, for example, Allen et al. [2], Wallach and Allen [102], Pereira and Saptawijaya [84], Pereira and Saptawijaya [83], Berreby et al. [15], Danielson [33], Lin [68], Malle et al. [73], Saptawijaya and Pereira [89], Saptawijaya and Pereira [90], Bentzen [14], Bhargava and Kim [16], Casey [26], Cointe et al. [29], Greene [48], Lindner et al. [69], Santoni de Sio [88], Welsh [103], Wintersberger [104], Bjørgen et al. [17], Grinbaum [51], Misselhorn [75], Pardo [82], Sommaggio and Marchiori [93], Baum et al. [13], Cunneen et al. [31], Krylov et al. [63], Sans and Casacuberta [87], Wright [105], Agrawal et al. [1], Agrawal et al [9], Banks [11], Bauer [12], Etienne [40], Gordon [46], Harris [52], Lindner et al. [70], Nallur [77]. And, several more articles that discuss trolley problems without citing Foot; e.g., Bonnefon et al. [20], Etzioni and Etzioni [41], Lim and Taeihagh [67], Evans et al. [42]; or, which appear to reinvent the trolley problem (without citing Foot); e.g., Keeling [59].

  6. There are obvious and perhaps pressing philosophical questions that arise concerning some of these classifications, but we will put those aside for now.

  7. Of course, this approach raises all the usual problems of biased data, insofar as certain individuals are going to be overrepresented—i.e., those individuals from countries who have easy access to the internet. Falbo and LaCroix [43] argue that these considerations may exacerbate structural inequalities and mechanisms of oppression—although, they also note that ‘more data’ is not necessarily going to fix that, since the data are reflective of extant inequalities in society. However, it is crucial to note that the thing being measured in this case is not how ethical the decision is, but how closely the decision accords with the opinions of humans, on average.

  8. There is a vast metaphilosophical literature on the role and purpose of thought experiments in philosophy. I do not have the space to delve into any adequate detail here; however, see Brown and Fehige [23] for an overview. See also Asikainen and Hirvonen [8] for a discussion of thought experiments in the context of science.

  9. See, for example, Greene et al. [47, 49, 50], Nichols and Mallon [79], Cushman et al. [32], Schaich Borg et al. [91], Ciaramelli et al. [28], Hauser et al. [53], Koenigs et al. [61], Waldmann and Dieterich [101], Moore et al. [76].

  10. Surveys have shown that a vast majority would choose to pull the switch in this scenario [22, 78].

  11. For example, if it were determined that a utility calculus is the ‘correct’ normative theory, then we could use moral dilemmas as a validation tool. However, no such determination has been made.

  12. Note that in response to the problem of enabling autonomous systems to distinguish between available choices and to choose the ‘least unethical’ one, Dennis et al. [37] suggest that the pressing question to be resolved is ‘how can we constrain the unethical actions of autonomous systems but allow them to make justifiably unethical choices under certain circumstances?’ But this presupposes that we already know what it means for a decision to be the least unethical. As is common, Dennis et al. [37] seem to understand ‘least unethical’ in terms of ‘least unacceptable’ by the standards of some subset of society.

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  1. Department of Philosophy, Dalhousie University, Halifax, Canada

    Travis LaCroix

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LaCroix, T. Moral dilemmas for moral machines. AI Ethics 2, 737–746 (2022). https://doi.org/10.1007/s43681-022-00134-y

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